Emmanuel Duflos

Emmanuel Duflos

I am Full Professor at Ecole Centrale de Lille where I am the Deputy Director and Director of Research.
I mainly teach at graduate level: bayesian estimation,
Sequential Monte Carlo and MCMC methods.

My research activity focus on statistical signal processing, bayesian modeling and analysis and
multi-objects filtering. One of my major aim is to show the benefits of Bayesian Non Parametric methods
in the field of signal processing. I’ve showed how this framework can be successfully use to mitigate
multipath effects in GNSS/GPS localizations algorithms.

DEA (Master Level) – Signal Processing and Automatic Control

1991 - 1992

1988 - 1991

Experience

Full Professor, Deputy Director and Director of Research Ecole Centrale de Lille

- Member of the executive board
- Participation to the definition and implementation of the general strategy of Ecole Centrale
de Lille
- Responsible for the definition and implementation of the scientific policy of Ecole Centrale
de Lille. The research areas are : mechanics, material, catalyst, electronics, electricity,
nanotechnology, control, statistical analysis, business modeling
- Head of the "Signal and Image Processing" group (22 people - permanent staff) of the LAGIS
Laboratory (UMR CNRS 8219) (see Signal & Image)
- Member of the executive board of LAGIS
- Personal research activity in Statistical Signal Processing and Bayesian Analysis with a main
application to GPS localization enhancement.
- Teaching in Signal Processing and Bayesian Analysis

From 2011

Full Professor, Deputy Director and Director of Information Technology Ecole Centrale de
Lille

- Member of the executive board
- Participation to the definition and implementation of the general policy of Ecole Centrale de
Lille as Deputy Director
- The job as Director of Information Technology is the same as the one over 2006 - 2010
- Research: Statistical Signal Processing and Bayesian Analysis
- Head of the "Signal and Image" group of the LAGIS Laboratory (UMR CNRS 8219)
- Teaching: Statistical Signal Processing and Bayesian Analysis.

2006 - 2010

Full Professor, Director of the computer center Ecole Centrale de Lille

- Head of the computer center
- Research: Statistical Signal Processing and Bayesian Analysis
- Teaching: Statistical Signal Processing and Bayesian Analysis
- Head of the last year specialization program in "Computer Science" at IG2I (a department of
Ecole Centrale de Lille)
- Participation to the creation of the INRIA project team SequeL devoted Sequential Learning.
SequeL hosted by the INRIA Lille Nord Europe.

Full Professor Ecole Centrale de Lille

2003 - 2004

Head of a Research and Teaching Department in Automatic Control and Signal Processing
Institut Supérieur de
l’Électronique et du Numérique

- Member of the executive board
- Head of the "Signal and System" Department
- Definition and implementation of the department strategy in both Research and Teaching
- Management of the department permanent staff (4 people) and supply teachers (15)
- Responsible for the budget and fundings (around 100 000€/year) of the department
- Research: Statistical Signal Processing and Bayesian Analysis
- Teaching: Statistical Signal Processing, Bayesian Analysis, automatic control (~600h/year)
- Head of the last year specialization program in Signal Processing and Automatic Control.

1999 - 2003

1995 - 1999

Research

I mainly develop new Bayesian methods for analysis and estimation purposes. The resulting
algorithms are themselves based on Sequential Monte Carlo and MCMC methods for
which
evolutions are also proposed. The originality of the work carried out are:

1. The development of Bayesian non parametric (BNP) methods for signal processing. My team
and I
have been the first to propose BNP methods for estimation purpose in non gaussian dynamical systems.
The
seminal publication on BNP in IEEE Transactions in Signal Processing has been cited 80 times (Google
scholar) since its publication in 2008. Such a framework has been used successfully to derive new
state
Bayesian estimators to mitigate both multipath noise in GNSS localization and impulsive
(alpha-stable)
noise.

2. The development of multisensor multi target tracking methods based on finite random sets
(like
the Probability Hypothesis Density (PHD) filter) . This activity is more recent.

Bayesian Non Parametric Estimation
Numerous problems in signal processing may be solved efficiently by way of a Bayesian approach. The
use
of Monte-Carlo methods let us handle non linear, as well as non Gaussian problems. In their standard
form, they require the formulation of densities of probability in their parametric form. For
instance,
it is a common usage to use Gaussian likelihood, because it is handy. However, in some applications
such
as Bayesian filtering, or blind deconvolution, the choice of a parametric form of the density of the
noise is often arbitrary. If this choice is wrong, it may also have dramatic consequences on the
estimation quality. To overcome this shortcoming, one possible approach is to consider that this
density
must also be estimated from data. A general Baysesian approach then consists in defining a
probabilistic
space associated to the possible outcomes of the object to be estimated. Applied to density
estimation,
it means that we need to define a probability measure on the probability density of the noise : such
a
measure is called a random measure. The classical Bayesian inference procedures can then been used.
This
approach being by nature non parametric, the associated frame is called Non Parametric Bayesian.

In particular, mixtures of Dirichlet processes provide a very powerful formalism. Dirichlet
Processes
are a possible random measure and Mixtures of Dirichlet Processes are an extension of well-known
finite
mixture models. The class of densities that may be written as a mixture of Dirichlet processes is
very
wide, so that these are really fit to very large amount of applications. Given a set of
observations,
the estimation of the parameters of a mixture of Dirichlet processes is performed by way of a Monte
Carlo Markov Chain (MCMC) algorithm. Dirichlet Process Mixture are also widely used in clustering
problems. Once the parameters of a mixture are estimated, they can be interpreted as the parameters
of a
specific cluster defining a class as well. Dirichlet processes are well known within the machine
learning community and its potential in statistical signal processing still need to be developped.

Multi-object filtering : Probability Hypothesis Density filter
In the general multi-sensor multi-target Bayesian framework, an unknown (and possibly varying)
number of
targets whose states are observed by several sensors which produce a collection of measurements at
every
time step. Well-known models to this problem are track-based models such as the joint probability
data
association (JPDA) or joint multi-target probabilities such as the joint multi-target probability
density. Common difficulties in multi-target tracking arise from the fact that the system state and
the
collection of measures from sensors are unordered and their size evolve randomly through time.
Vector-based algorithms must therefore account for state coordinates exchanges and missing data
within a
unknown time interval. Although this approach is very popular and has resulted in many algorithms in
the
past, it is not the optimal way to tackle the problem since the sate and the data are in fact sets
and
not vectors.

The random finite set theory provides a powerful framework to cope with these issues. Mahlerís work
on
finite sets statistics (FISST) provides a mathematical framework to build multi-object densities and
derive the Bayesian rules for state prediction and state estimation. Randomness on object number and
their states are encapsulated into random finite sets (RFS), namely multi-target(state) sets and
multi-sensor (measurement) set. The objective is then to propagate the multitarget probability
density
by using the Bayesian set equations at every time step. Unfortunately, although these equations may
seem
similar to the classical single-sensor/single-target Bayesians equations, they are generally
untractable
because of the presence of the set integrals. For, a RFS is characterized by the family of its
Janossy
densities and not just by one density as it is the case with vectors. To solve this problem, Mahler
introduced the PHD, defined on single-target state space. The PHD is the quantity whose integral on
any
region is the expected number of targets inside this region. Mahler proved that the PHD is the
first-moment density of the multi-target probability density.